Abstract
The fundamental goal of this research is to learn whether unsupervised learning can be used to cluster documents in the collection in a similar way that manual categories are. We report on our experiments with K-mean clustering algorithm to provide a partial answer to the above mentioned goal.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kanungo T., Mount D. M., Netanyahu N., Piatko C., Silverman R. and Wu A. Y. (2002) An Efficient k-Means Clustering Algorithm: Analysis an Implementation. In: IEEE Trans. PAMI vol. 24, pp. 881–892.
McCallum A. (1996) Bow: A Toolkit for Statistical Language Modeling, Text Retrieval, Classification and Clustering. http://www.cs.cmu.edu/~mccallum/bowl.
McCallum A. and Nigam K. (1998) A Comparison of Event Models for Naïve Bayes Text Classification. In AAAI-98 Workshop for Text Categorization.
Ranka S. and V. Singh (1998) An Efficient K-means Clustering Algorithm. In: Proc. First Workshop High Performance Data Mining.
Taghva K. and Gilbreth J. (1995) Reconizing Acronyms and their Defintions. http://www.isri.unlv.edu/publications/isripub/Taghva95-03.ps.
University of California, Irvine. Reuter 21578 Text Categorization Collection. http://kdd.ics.uci.edu//databases/reuters21578/reuters21578.html.
VanRijsbergen C. J., Robertson S. E., and Porter M. F. (1980) New models in probabilistic information retrieval. British Library, London, British Library Research and Development Report, no. 5587.
Wikipedia, The Free Encyclopedia, Data Clustering. http://en.wikipedia.org/wiki/Data clustering.
Weisstein, Eric W. K-Means Clustering Algorithm. In: Math World-A Wolfram Web Resource http://mathworld.wolfram.com/K-MeansClusteringAlgorithm.html.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag London Limited
About this paper
Cite this paper
Taghva, K., Sharma, M. (2007). Comparison of Automatic Clustering and Manual Categorization of Documents. In: Akhgar, B. (eds) ICCS 2007. Springer, London. https://doi.org/10.1007/978-1-84628-992-7_26
Download citation
DOI: https://doi.org/10.1007/978-1-84628-992-7_26
Publisher Name: Springer, London
Print ISBN: 978-1-84628-990-3
Online ISBN: 978-1-84628-992-7
eBook Packages: Computer ScienceComputer Science (R0)